• The Journal of the Korea Contents Association
• /
• v.21 no.6
• /
• pp.639-652
• /
• 2021

In accordance with the rapidly changing social environment, it is becoming more important to cultivate creative and convergent practical talents with flexible thinking skills and problem-solving skills. Therefore, it is necessary for universities to provide educational experiences that enable students to cooperate and converge multidisciplinaryly to carry out on-the-job projects based on what they have learned at school. Therefore, this study designed, developed, and operated with the aim of cultivating creative talents with integrated problem-solving ability through a multidisciplinary capstone design curriculum based on industry-academia cooperation. To this end, the curriculum was developed together by recruiting participating companies and forming a convergence professor team, and it was operated for 15 weeks for students majoring in cosmetics engineering at D University. After the education was over, learning satisfaction and perceived academic achievement were surveyed, and as a result of the analysis, it was found to be above average with 3.77 points and 3.86 points, respectively. And as a result of the in-depth interview on the participation experience, five themes related to the positive experience and three themes related to the negative experience were derived. This study will be able to provide basic data when operating a multidisciplinary convergence capstone design curriculum based on industry-academia cooperation in the future.

• Korean Journal of Microbiology
• /
• v.39 no.4
• /
• pp.207-215
• /
• 2003

Gene structure prediction, which is to predict protein coding regions in a given nucleotide sequence, is the most important process in annotating genes and greatly affects gene analysis and genome annotation. As eukaryotic genes have more complicated stuructures in DNA sequences than those of prokaryotic genes, analysis programs for eukaryotic gene structure prediction have more diverse and more complicated computational models. We have developed EGSP, a eukaryotic gene structure program, using duration hidden markov model. The program consists of two major processes, one of which is a training process to produce parameter values from training data sets and the other of which is to predict protein coding regions based on the parameter values. The program predicts multiple genes rather than a single gene from a DNA sequence. A few computational models were implemented to detect signal pattern and their scanning efficiency was tested. Prediction performance was calculated and was compared with those of a few commonly used programs, GenScan, GeneID and Morgan based on a few criteria. The results show that the program can be practically used as a stand-alone program and a module in a system. For gene prediction of eukaryotic microbial genomes, training and prediction analysis was done with Saccharomyces chromosomes and the result shows the program is currently practically applicable to real eukaryotic microbial genomes.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.17 no.5
• /
• pp.81-94
• /
• 2022

This study analyzed the effect of the entrepreneurship planning course provided by an women's university in Seoul on the enhancement of the core competencies of entrepreneurship of university students. To this end, pre- and post-test of core entrepreneurship competency were conducted on 63 female university students (32 in experimental group, 31 in control group) and then the results were analyzed. The course in which the experimental group participated was a team-based project learning course and it required a team of three people to draw an entrepreneurship plan containing social problem solving as the final result. The course was operated for a total of 8 weeks. To measure the level of entrepreneurship core competency in the pre- and post- test, the survey tool that was developed by the Ministry of Education and Korea Entrepreneurship Foundation (2020) was used. This tool composed by 'value creation', 'challenge', 'self-directed', and 'group creativity' competencies. As analyses methods, i) covariance analysis was performed using the pretest as a covariate, and then a two-way ANOVA was performed with treatment (experimental group, control group) and time point (pre test, post test) as two independent variables. Results show while there was no significant difference between the experimental group and the control group in the value creation competency, it significantly contributed to the enhancement of challenge, self-directed, and collective creativity competencies. Based on these results, implications and limitations were discussed, followed by future research direction.

• The Journal of the Korea Contents Association
• /
• v.13 no.4
• /
• pp.481-496
• /
• 2013

The purpose of this study was to make a basic research on college cultural contents education in an effort to step up the manifestation of the creativity of cultural contents experts in line with the development of the fast-changing era of creative economy. It's basically meant to analyze the characteristics of cultural contents education in relation to creative idea to seek practical ways of improving that education. What problems there were with cultural contents education and how that education was actually provided were analyzed to suggest some of the right directions for client-centered cultural contents education. Earlier studies were analyzed, and the results of a survey that was conducted on students whose major was linked to cultural contents were analyzed as well. As a result, current cultural contents education was considered not to be satisfactory due to existing teaching methods, learning process and curriculums that were devoid of creativity. To rectify the situation, interdisciplinary attempts should be made such as multi-major, interdisciplinary programs or convergence education, and plenty of experiments, sufficient practice and an increase in the number of faculty members are all required. In terms of education, existing curriculums and courses should urgently be revamped to strengthen field placement and creative discussions. As for educational methods, the lecture method should be avoided, and specialized education should be offered instead, which should strike a balance between discussion, team play and project education. It is expected to produce good results if there are appropriate connection among different major fields of study and the harmonious implementation of diverse internship, convergence and field placement programs.

• Journal of Intelligence and Information Systems
• /
• v.26 no.4
• /
• pp.27-65
• /
• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.